Fall 2024 Course Offerings
| BIOE 720-701 |
Biostatistics for Public Health - for students in the Certificate in Clinical Research program |
Chi-Yang Chiu, PhD |
| Time:Place:Duration:
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Credit:3 |
| This course provides an introduction to the use of statistical techniques in biomedical and public health research. The course will cover common
descriptive statistics including the mean, median, and standard deviation as well as techniques for testing hypotheses (analysis of variance, t-tests,
regression, nonparametric methods) and issues in sampling and design of experiments. The class will be taught using online methods and is open only for
students enrolled in programs of the Tennessee Consortium for Public Health Workforce Education. (online, lecture)
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| BIOE 727 |
Principles of Epidemiology |
Alex Mason, PhD |
| Time:Place:Duration:
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Credit:3 |
| This online course introduces the basic principles and methods of epidemiology and
demonstrates their applicability in the fields of public health and clinical research. Topics to be covered include the historical perspective of
epidemiology, measures of disease occurrence and association, study design, disease screening, and causal inference. Study design content will cover
experimental, cohort and case-control studies as well as challenges in design and analysis including bias, confounding and random error. Students will be
expected to participate in discussion boards, complete weekly homework assignments, and take a mid-term and final exam. (online, lecture)
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| BIOE 800 |
Master's Thesis and Research |
Simonne Nouer, MD, PhD |
| Time:Place:Duration:
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Credit:variable |
| Research performed under the direction and supervision of the respective student's Research Advisor, in partial fulfillment of the requirements for the degree of Master of Science. (didactic, research)
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| BIOE 804 |
Master's Project |
Simonne Nouer |
| Time:Place:Duration:
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Credit:variable |
| Students will work on their master?s project in conjunction with advisor and master?s committee. Research-based course. Credit variable (1-6) as
assigned by instructor. (didactic, research)
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| BIOE 805 |
Using R for Biostatistics I |
Hyo Young Choi |
| Time:Place:Duration:
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Credit:2 |
| The course will introduce students to R, a versatile open-source
language and programming environment. R is widely used for data
analysis and visualization by statisticians and data scientists. This
course will introduce students to the basics of R language, Students
should have a thorough understanding of basic statistics at the level
of BIOE811 and BIOE821 (Biostatistics for the Health Sciences I and
II). (didactic, lecture)
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| BIOE 810 |
Independent Study |
Simonne Nouer |
| Time:Place:Duration:
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Credit:variable |
| An in-depth study of some aspect of epidemiology in which the student has special interest. Study is done independently with faculty approval and supervision. (didactic, independent)
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| BIOE 811 |
Biostatistics for the Health Sciences I |
Betsy Tolley, PhD |
| Time:Place:Duration:
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Credit:4 |
| This course provides students with an introduction to descriptive statistics, probability and probability distributions, estimation, and one and two sample hypothesis testing, including paired and unpaired situations, for normally distributed and ordinal data. Students will also be introduced to one-way analysis of variance, including multisample inference, one-way ANOVA, fixed-effect and random effects models, and intraclass correlation coefficients. This course also includes a mandatory statistical computing laboratory that uses SAS for data analysis throughout the semester. (online, lecture lab)
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| BIOE 812 |
Fundamentals of Epidemiology |
Simonne S. Nouer, MD, PhD |
| Time:Place:Duration:
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Credit:3 |
| The course introduces the basic principles and methods of epidemiology and demonstrates their applicability in the field of public health. Topics to
be covered include the historical perspective of epidemiology, measures of disease occurrence and of association, clinical epidemiology, disease screening,
causal inference, and study design. (online, lecture)
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| BIOE 813 |
Fundamentals of SAS for Epidemiology |
Jim Y. Wan, Ph.D. |
| Time:Place:Duration:
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Credit:3 |
| This course provides the foundation computing skills for independent analysis of epidemiologic data. Topics to be covered include an introduction to
SAS as a research tool; Operating with SAS for Windows environment; Reading internal and external data into SAS; Working with variables and SAS functions;
Using logical statements; Introducing SAS procedures - especially those that produce descriptive statistics; Performing simple inferential tests and power
analysis; combining datasets; Reshaping data; and Introducing macro language. This course consists of 2 hour lecture and 1 hour laboratory session per
week. (online, lecture lab)
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| BIOE 823 |
Randomized Clinical Trials |
Mathilda Coday |
| Time:Place:Duration:
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Credit:3 |
| This course will allow the student to understand and analyze the many critical facets of the most precise design for clinical studies in humans:
randomized clinical trials. Using a case-based approach, students will learn the importance of precise hypothesis description, selection of an at risk cohort
for study, and the power of randomization in helping balance the study groups on a number of known and unknown confounding factors. Important issues with
regard to subject recruitment, patient management, and data quality control will be emphasized. Students will learn to perform their own sample size
calculations and use actual statistical packages to outline real clinical trial results data. (hybrid, lecture)
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| BIOE 848 |
Professional Experience and Prior Learning Assessment |
Simonne Nouer |
| Time:Place:Duration:
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Credit:variable |
| This course recognizes that work experience can provide valuable learning experiences that can complement learning acquired through formal education.
This course offers an assessment of experiential learning, performed through the construction of a portfolio, that emphasizes the connection between learning
from work experience, practice skills, continuing education, clinical investigatory knowledge, and its translational application to research. This course
cannot be repeated. Variable credit 1-3 hours. (didactic, lecture)
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| BIOE 850 |
Categorical Data Analysis |
Mehmet Kocak |
| Time:9:00 A.M.-10:30 A.M.Place:Preventive Medicine SAS LabDuration:Once a week for 10 weeks with 1 hour face-to-face in each meeting, with 2 hours of course materials provided each week online. |
Credit:2 |
| This course is a 2-credit hour hybrid course, where the students will receive 2-hour worth of online course materials and will have a weekly one-hour face-to-face session with the instructor. The course begins with an introduction and review of most common discrete random variables and their probability distributions, followed by a brief discussion of ‘parameter estimation’ as a general concept in Theoretical Statistics. Then, we introduce the concept of inferential statistics by discussing one-sample confidence interval and hypothesis testing for one- and two-sample designs, which includes the definition of and testing for statistical independence through the most commonly used chi-square-based tests for 2x2, Rx2, 2xC, and RxC contingency tables and sets of (stratified) contingency tables. Then, the generalized linear model is introduced as the backbone for model building that focuses on the estimation of effects of one or more predictors on a binary response variable or on a count variable, including model inference and model diagnostics checking. Specific topics for the modeling of categorical data include logistic regression for dichotomous and polytomous response, conditional logistic regression, generalized estimating equations, and generalized linear mixed modeling for models with random effects. In addition, the course will explore log-linear modeling for count data. The relation of the various approaches and procedures using SAS will be demonstrated. The course focuses on application of the above approaches to observational and clinical trial designs.
At the end of the semester, a letter grade will be assigned to each student according to the following system of grades, with equivalent quality point value, is adopted:
A (4-0); A- (3.67); B+ (3.33); B (3.00); B- (2.67); C+ (2.33); C (2.00); C- (1.67); D (1.00);
and F (0) (as specified at http://www.uthsc.edu/grad/CollegeInfo/index.php?page=Bylaws#Grading). (hybrid, lecture)
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| BIOE 852 |
Introduction to Biostatistics for the Health Sciences |
Xueyuan Cao, PhD |
| Time:TBDPlace:online meetingsDuration:16 weekly conferences of about 1 hour |
Credit:3 credit hours |
| This introductory course provides a basic foundation for further coursework in biostatistics. It is provides students with a step-by-step, hands-on approach to using data for statistical analysis. This course emphasizes how to ask appropriate research questions and interpret statistical results. Statistical methodology includes a thorough introduction to the meaning of relevant terms, assumptions, statistical computations, and appropriate interpretation of results. Students must be able to read and understand algebraic equations. (online, lecture independent)
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| BIOE 864 |
Data Science 2: Statistical Methods for Observational Studies |
Fridtjof Thomas |
| Time:Place:Duration:
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Credit:1 |
| This second course in data science Data Science 2: Statistical Methods for Observational Studies focuses on statistical approaches in data science, especially those relating to observational studies. This course can also be chosen independently for those students that wish to learn about observational studies in general. Students will learn about the distinction of causal analysis vs. association studies and the consequences for appropriately choosing statistical methods for data analysis. Sources of bias in observational studies and statistical methods to tackle these are discussed. This 1 credit course is taught with hands-on exercises and the student is expected to be comfortable with algorithmic approaches and computer programming. (didactic, lecture)
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| BIOE 865 |
Linear Regression Methods for the Health Sciences |
Betsy Tolley, PhD |
| Time:Tuesday & Thursday, Noon - 2 PMPlace:Room 400, 66 North Pauline StreetDuration:16 meetings of 2 hours each |
Credit:2 |
| In this course, students will learn how multiple linear regression models are derived, use software to implement them, learn what assumptions underlie the models, learn how to test whether data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models. (hybrid, lecture)
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| BIOE 866 |
Linear Mixed Models |
Tamekia Jones |
| Time:Tuesday & Thursday, 1-3 pmPlace:Duration:14 lectures for 2 hours each |
Credit:2 |
| This course provides the advanced skills necessary for independent statistical analysis of epidemiologic and clinical data containing clustered observations and random effects. Topics to be covered include unrestricted and restricted maximum likelihood estimation, Akaike’s information criterion, standard general linear models, linear random effects models, linear covariance pattern models, and linear random coefficient models. The course focuses on applications requiring flexible modeling of variance and covariance structures for clustered data when observations from a common cluster are correlated. The approaches covered in the course are particularly relevant for analysis of hierarchical and longitudinal data having Gaussian distributed error (, lecture)
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Spring 2025 Course Offerings
| BIOE 729 |
Introduction to Health Disparities |
Shelley White-Means, PhD |
| Time: Place: Duration: |
Credit: 3 |
| This course will provide an overview of health disparities and the historical underpinnings of health disparities as well as an examination of the social determinants of health in the United States. (online, lecture)
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| BIOE 750 |
Fundamentals of Clinical Investigation |
Mace Coday, Ph.D. |
| Time: Place: Duration: |
Credit: 3 |
| This course will present an introduction to the different types of clinical research and practical methods that investigators can use in the conduct
of multidisciplinary clinical research. Observational cohort studies, case-control studies, and Phase I-IV intervention-based randomized controlled clinical
trials will be presented. Design distinctions, sampling and randomization procedures, data integrity, data-analysis concerns, and practical conduct for these
investigative approaches will be examined. This course will also review ethical issues in conducting research in people, federal guidance for the conduct of
clinical research, and the dynamic influence of behavior on the conduct of clinical research. This is an online course for the web-based Certificate in
Clinical Research program. (online, lecture)
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| BIOE 800 |
Master's Thesis and Research |
Simonne Nouer |
| Time: Place: Duration: |
Credit: variable |
| Research performed under the direction and supervision of the respective student's Research Advisor, in partial fulfillment of the requirements for the degree of Master of Science. (didactic, research)
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| BIOE 804 |
Master's Project |
Simonne Nouer |
| Time: Place: Duration: |
Credit: variable |
| Students will work on their master?s project in conjunction with advisor and master?s committee. Research-based course. Credit variable (1-6) as
assigned by instructor. (didactic, research)
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| BIOE 806 |
Using R for Biostatistics II |
Saunak Sen |
| Time: Place: Duration: |
Credit: 2 |
| This is a second course in R, a versatile open-source language and
programming environment. R is widely used for data analysis and
visualization by statisticians and data scientists. This course will
delve into the details of R programming focussing on the powerful
model formula syntax for specifying statistical models, implementing
generalized linear models, and data wrangling. Students should be
familiar with R at the level of BIOE805 (R for Biostatistics I). (didactic, lecture)
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| BIOE 810 |
Independent Study |
Simonne Nouer |
| Time: Place: Duration: |
Credit: variable |
| An in-depth study of some aspect of epidemiology in which the student has special interest. Study is done independently with faculty approval and supervision. (didactic, independent)
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| BIOE 821 |
Biostatistics for the Health Sciences II |
Elizabeth Tolley |
| Time: Place: Duration: |
Credit: 4 |
| Second semester content pertains to methods of regression for observational and experimental data. Methods of analysis and hypothesis testing for
three or more treatments are presented for various experimental designs and treatment combinations for normally distributed and ordinal data. Instruction
includes helping the students attain mastery-level skill in programming with the SAS software system for statistical analysis of data. (online, lecture)
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| BIOE 822 |
Advanced Epidemiology |
Simonne Nouer |
| Time: Place: Duration: |
Credit: 4 |
| This course provides the foundation skills for independent analysis of epidemiologic data. Topics to be covered include the analysis of vital
statistics data, statistical analysis of simple epidemiologic measures, identification and control of confounding in epidemiologic data, model building using
epidemiologic data, logistic regression, and proportional hazards modeling. At the end of the semester, students will be able to analyze data from matched and
unmatched case-control studies, case-cohort studies, and traditional cohort designs. The course includes a mandatory statistical computing
laboratory. (online, lecture)
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| BIOE 824 |
Genetic Epidemiology: Methods and Applications |
Khyobeni Mozhui |
| Time: Place: Duration: |
Credit: 3 |
| This course provides the concepts and methods of genetic epidemiology that are relevant to studying the causes of complex human diseases and the
impact of human genetic variation on disease prevention and treatment. The course includes methods of population- and family-based studies of
genotype-phenotype associations; statistical techniques related to segregation analysis; linkage analysis and transmission disequilibrium test (TDT);
approaches for assessing gene-gene and/or gene-environment interaction; and procedures for evaluating ethical, legal, and social issues, and public health
implications of research and interventions. Emphasis is placed on distinguishing the appropriate applications, underlying assumptions, and reasonable
interpretations of the methods presented. (didactic, lecture)
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| BIOE 834 |
Epidemiology of Childhood Diseases |
Marion Hare, MD |
| Time: Place: Duration: |
Credit: 2 |
| This course will provide an overview of the epidemiology of selected conditions and diseases affecting children as well as demonstrate the childhood
origins of some adult chronic disease. For most of these conditions, information about the pattern of occurrence, data about risk factors and effectiveness of
various preventive or therapeutic interventions will be discussed. Public use sources of information such as the National Health and Examination Survey
(NHANES), National Ambulatory Medical Care Survey (NAMCS), CDC "Pink Book", Child and Adolescent Health Measurement Initiative (CAHMI) and Youth Risk Behavior
Surveillance System (YRBSS) will be introduced and discussed. Additionally, some of the unique and challenging aspects of research in pediatric epidemiology
such as issues of childhood growth and development, maternal (intrauterine) origins of disease and parental role in disease diagnosis and treatment will be
introduced. In the last weeks of the course, students will be asked to synthesize the information presented in the course by identifying, presenting and
evaluating the available epidemiological information on a childhood disease or condition of their choice. (hybrid, lecture)
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| BIOE 848 |
Professional Experience and Prior Learning Assessment |
Simonne Nouer |
| Time: Place: Duration: |
Credit: variable |
| This course recognizes that work experience can provide valuable learning experiences that can complement learning acquired through formal education.
This course offers an assessment of experiential learning, performed through the construction of a portfolio, that emphasizes the connection between learning
from work experience, practice skills, continuing education, clinical investigatory knowledge, and its translational application to research. This course
cannot be repeated. Variable credit 1-3 hours. (didactic, clinical)
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| BIOE 868 |
Survival Analysis |
Fridtjof Thomas |
| Time: Place: Duration: |
Credit: 2 |
| Survival analysis refers to the statistical approach to analyze the occurrence and timing of events. Students will gain familiarity with the characteristics of time-to-event data such as the presence of censoring and time-varying covariates, and will learn to master the necessary statistical techniques to design and analyze studies with survival data, including the construction and interpretation of Kaplan-Meier estimates and the Cox proportional hazards model. This course also extends the standard Cox model by introducing time-varying covariates and stratification as a way of dealing with non-proportionality of hazards. The course utilizes the software SAS and especially PROC LIFETEST and PROC PHREG. This 2 credit course is taught with hands-on exercises and the student is expected to bring his/her own computer with a fully functional SAS installation. (didactic, lecture)
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| BIOE 869 |
Data Science 3 |
Zhu Wang |
| Time: Place: Duration: 15 meetings, two hours each |
Credit: 2 |
| The seminar-based course will cover advanced topics in data science
reflecting the interest of participating students and faculty. The
seminars are designed to cover a wide variety of methodological topics
in data science from a statistical and informatics perspective.
(didactic, seminar)
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